Leveraging Social Foci for Information Seeking in Social Media
Authors: Suhas Ranganath, Jiliang Tang, Xia Hu, Hari Sundaram, Huan Liu
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Conducting experimental evaluations of the framework on a dataset of social media questions. |
| Researcher Affiliation | Academia | Suhas Ranganath srangan8@asu.edu Arizona State University Jiliang Tang Jiliang.Tang@asu.edu Arizona State University Xia Hu Xia.Hu@asu.edu Arizona State University Hari Sundaram hs1@illinois.edu University of Illinois Urbana Champaign Huan Liu Huan.Liu@asu.edu Arizona State University |
| Pseudocode | Yes | Algorithm 1: Automatic Identification of Answerers to Social Media Questions |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes the dataset and how it was collected using the public Twitter API, but it does not provide concrete access information (e.g., a link, DOI, or specific repository) for the collected and processed dataset itself. |
| Dataset Splits | No | The paper mentions 'train', 'validation', and 'test' in general terms related to machine learning concepts but does not specify the dataset splits (e.g., percentages, sample counts, or citations to predefined splits) used for these phases. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions algorithms (NMF, LDA, STM) but does not list any specific software dependencies or libraries with version numbers required to replicate the experiments. |
| Experiment Setup | Yes | For initial experiments, we set the parameters in Eq. (3) as follows. The regularization parameter is set at γ=0.01. The number of topics in the baselines and the number of foci k is set as 50. For initial evaluation of the framework, we choose α=1 and β=1. |